The environment is so rich with information that our cognitive system would be overloaded without a way to evaluate what is relevant for our needs and goals. Appraisal theory has shown how emotions, by "tagging" the environment with differential values, enable the attribution of our attentional resources to what is most relevant in any given circumstances. Most often, however, the different cue...

A person’s interests exist as an internal state and are difficult to define. Since only external actions are observable, a proxy must be used that represents someone’s interests. Techniques like collaborative filtering, behavioral targeting, and hashtag analysis implicitly model an individual’s interests. I argue that these models are limited to shallow, temporary interests, which do not reflec...

Recommender systems are essential to overcome the information overload problem in professional learning environments. In this paper, we investigate interest-based recommendation in academic networks using social network analytics (SNA) methods. We implemented 21 different recommendation approaches based on traditional Collaborative Filtering (CF), Singular Value Decomposition (SVD)-based RS, Tr...

Social Networking Services (SNSs) have been gaining popularity in recent years. However, as the number of users of SNS increases, users are forced to spend a great deal of time in order to nd a good friend . The user recommendation system is a good tool that solves this problem, but in that case, the system should understand the user's interests. In the present paper, as a means of extracting a...

With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have ...

We propose a method to predict users’ interests in social media, using time-evolving, multinomial relational data. We exploit various actions performed by users, and their preferences to predict user interests. Actions performed by users in social media such as Twitter, Delicious and Facebook have two fundamental properties. (a) User actions can be represented as high-dimensional or multinomial...

Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The high-dimension content generated by these users provides an unique opportunity to understand their behavior deeply. As one of the most fundamental topics, location estimation attracts more and more research efforts. Different from the previous literature, we find that user...